Interest in the research community on the plausibility of predicting the aesthetic quality of images has increased dramatically over the past few years. It was established in Datta et al. (2006) that photo aesthetics, though being subjective, can be estimated using a set of images with a general consensus on their aesthetic quality. Mathematical models could be developed which can predict the aesthetics of any image.
Understanding aesthetics can aid many of the applications like summarization of photo collections (Obrador et al. 2010), selection of high quality images for display (Fogarty et al. 2001) and extraction of aesthetically pleasing images for image retrieval (Obrador et al. 2009). It can also be used to render feedback to the photographer on the aesthetics of his/her photographs. Many other applications have been built around suggesting improvisations to the image composition (Bhattacharya et al. 2010; Liu et al. 2001) through image retargeting, and color harmony (Cohen-Or et al. 2006) to enhance image aesthetics. These applications are more on-line in nature, though they are able to provide useful feedback, it is not on the spot, and requires considerable input from the user. There is no scope for any improvement on the images captured once the user moves away from the location which a professional feedback on-site can accomplish.